Detecting Key Inter-Joint Distances and Anthropometry Effects for Static Gesture Development using Microsoft Kinect

Abstract

In the effort to design three-dimensional gestural interfaces for surface ship platforms, this study was conducted to evaluate the critical anthropometric factors that influence developing mutually exclusive static posture recognition algorithms. Using the Microsoft Kinect, 400 inter-joint distances determining critical key joint factors were collected to identify 15 static postures. By using a discriminant analysis, 9 out of 400 possible key inter-joint distances were identified for distinguishing the 15 postures. Comparing the discriminant function model predictions to the actual observed values for the 15 postures resulted in an overall prediction accuracy of 97 percent.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2013
Accession Number
ADA595180

Entities

People

  • Angela Nunnally
  • Jake Quartuccio
  • Michael A. Hamilton
  • Patrick Mead
  • Rachael Lund

Organizations

  • Naval Surface Warfare Center

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • C4I
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Anthropometry
  • Cognitive Systems Engineering
  • Computers
  • Data Science
  • Department Of Defense
  • Detection
  • Discriminant Analysis
  • Human Body
  • Human-Machine Interaction
  • Information Science
  • Motion Capture
  • Recognition
  • Statistics
  • Three Dimensional
  • Two Dimensional

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Maritime Combat Support and Expeditionary Logistics.
  • Regression Analysis.